Deep Convolution Neural Networks, or Deep CNN is the most core of the remarkable development in the field of Deep Learning. Though the CNN has been employed to solve character recognition problems in 1990s, it is not until recently that the CNN has become widespread in Machine Learning. For example, in 2012, the CNN significantly outperformed its competitors in an annual software contest, the ImageNet Large Scale Visual Recognition Challenge, and won the contest. After that, the CNN has become a very useful tool in the field of machine learning.
Meanwhile, image segmentation is a method of generating a label image using an input image, e.g., a training image, a test image. Recently, the Deep Learning has been so much widespread that the Deep Learning is being applied to the image segmentation.
By the way, when learning CNNs to detect obstructions in autonomous driving circumstances, a learning device has to learn a variety of objects which may be faced in the autonomous driving circumstances. And for this, training images should include a variety of objects which may be faced in the autonomous driving circumstances.
In a real driving circumstances, there may be variety of objects on the road, but it is not easy to collect these data. In other words, training images including unique objects which appears hardly in the road may not be collected easily from normal driving video data. For example, images of human, bicycles, or cars may be acquired easily from the normal driving video data so that the learning device may learn parameters of CNN by using training images with these common objects, but images of a tiger, or alligator may not be acquired easily from the normal driving video data so that the learning device may have difficulty in learning the parameters of CNN by using training images with these unique objects.